arXiv Computer Vision

SetCon: Towards Open-Ended Referring Segmentation via Set-Level Concept Prediction

SetCon reformulates open-ended referring segmentation as set-level concept prediction, using LVLM-generated natural-language concepts instead of segmentation-specific tokens. It employs a hierarchical semantic decomposition that first predicts a shared set-level concept and then refines it into fine-grained concept groups aligned with target subsets. The method, supported by a two-stage annotation pipeline adding hierarchical semantic supervision, achieves state-of-the-art results on multiple image and video benchmarks, with performance gains increasing as the number of referred targets grows.

arXiv Computation and Language

Sep 17

PANORAMA: Panoptic Grounded Captioning via Mask Proposal Selection

PANORAMA introduces a new panoptic grounded captioning framework that jointly generates detailed image captions and associates each phrase with precise pixel-level masks. The authors create PanoCaps, a human‑annotated benchmark with dense captions and near‑complete pixel coverage, and propose a phrase‑mask matching protocol with a generalized Panoptic Quality metric. PANORAMA conditions a pretrained segmenter on contextualized phrase representations, learns to select appropriate masks, and achieves state‑of‑the‑art grounding performance on PanoCaps and other pixel‑level tasks.

By Sara Pieri, Evangelos Kazakos, Shizhe Chen, Josef Sivic, Cordelia Schmid

llms computer-vision multimodal benchmarks

Hugging Face Trending Papers

Sep 16

PANORAMA: Panoptic Grounded Captioning via Mask Proposal Selection

Intelligent systems that act in the world require image understanding that is both comprehensive and spatially grounded. Current vision-language models (VLMs) can generate fluent and detailed image ca...

llms computer-vision multimodal benchmarks

arXiv AI

Aug 11

Vision-Language Grounding as Bidirectional Concept Correspondence

arXiv:2608. 07886v1 Announce Type: cross Abstract: Vision-language grounding connects language to visual content, yet most existing formulations reduce grounding to a unidirectional localization problem: given a prespecified text phrase or category name, identify the corresponding image region.

By Jieyu Zhang, Ziqi Gao, Luke Zettlemoyer, Ranjay Krishna

llms computer-vision multimodal safety

arXiv AI

Oct 15

UnSAMv2: Self-Supervised Learning Enables Segment Anything at Any Granularity

UnSAMv2 is a self‑supervised extension of the Segment Anything Model that can segment objects at any granularity without human annotations. It discovers mask‑granularity pairs and introduces a granularity control embedding for continuous scale adjustment, requiring only 6K unlabeled images and 0.02% extra parameters. The model outperforms SAM/SAM‑2 across 11+ benchmarks, improving metrics such as 1‑IoU from 58.0 to 73.1 and achieving state‑of‑the‑art performance on language‑guided segmentation.

By Junwei Yu, Trevor Darrell, XuDong Wang

llms rag computer-vision multimodal benchmarks

arXiv Computer Vision

Aug 25

Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos

arXiv:2501.04001v4 Announce Type: replace Abstract: This work presents Sa2VA, the first comprehensive, unified model for dense grounded understanding of both images and videos. Unlike existing multi-...

By Haobo Yuan, Xiangtai Li, Tao Zhang, Yueyi Sun, Zilong Huang, Shilin Xu, Shunping Ji, Yunhai Tong, Lu Qi, Jiashi Feng, Ming-Hsuan Yang

llms computer-vision fine-tuning multimodal benchmarks

arXiv Computer Vision

Sep 22

RefAM: Attention Magnets for Zero-Shot Referral Segmentation

arXiv:2509.22650v3 Announce Type: replace Abstract: Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models,...

By Anna Kukleva, Enis Simsar, Alessio Tonioni, Muhammad Ferjad Naeem, Federico Tombari, Jan Eric Lenssen, Bernt Schiele

llms diffusion computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jun 2

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.

By Bingyu Li, Da Zhang, Tao Huo, Zhiyuan Zhao, Junyu Gao, Xuelong Li

llms agents computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jun 26

Event-Aware Instructed Assistant for Referring Video Segmentation

arXiv:2606. 26994v1 Announce Type: cross Abstract: Existing referring video segmentation methods often treat a video as a single event consisting of multiple images, overlooking the fact that a video typically contains multiple distinct events.

By Jinyu Liu, Henghui Ding, Shuting He, Yu-Gang Jiang

computer-vision benchmarks safety

arXiv Computer Vision

Sep 4

FoRIS: Progressive Foreground Refinement for Training-Free In-Context Segmentation

FoRIS is a training‑free in‑context segmentation framework that refines foreground masks through a coarse‑to‑fine process. It operates in three stages—Foreground Purification, Localization, and Consolidation—to suppress background noise, pinpoint target regions, and reconstruct complete foreground structures. The method achieves state‑of‑the‑art performance, improving mIoU by 4.5 and 4.8 points in 1‑shot and 5‑shot settings respectively.

By Ming Hu, Jianfu Yin, Mingyu Dou, Miaomiao Zhang, Yao Wang, Cong Hu, Bingliang Hu, Quan Wang

computer-vision benchmarks

arXiv Computer Vision

3d ago

SplatScout: Query-Driven Part-Level Referring Segmentation in Gaussian Scenes

SplatScout is a new query‑driven, closed‑loop approach for part‑level referring segmentation in Gaussian scenes, placing perception under vision‑language model control. It actively selects views, directs segmentation, and refines predictions by inspecting rendered 3D results. On the newly introduced PartRef‑ScanNet++ benchmark, SplatScout achieves 0.538 mIoU, outperforming existing methods and demonstrating the benefits of iterative refinement and effective tool‑feedback utilization.

By Yihan Zang, Da Li, Shinkyu Park, Ivan Viola, Dominik Engel

llms computer-vision multimodal benchmarks

arXiv Computer Vision

Sep 17

Decoder-Agnostic Token Merging for Vision Transformers: A Systematic Study of G2TM

The paper studies Graph-Guided Token Merging (G2TM), a module that reduces token count in Vision Transformers. It evaluates G2TM across multiple segmentation frameworks and decoder types, finding that its performance gains are tied to the encoder rather than the decoder. The authors report consistent reductions in GFLOPs (22‑47%) and throughput improvements (up to 74%) on ADE20K, with optimal hyperparameters depending mainly on backbone pre‑training and target dataset.

By Victor Bercy, Martyna Poreba, Michal Szczepanski, Samia Bouchafa

llms computer-vision efficiency benchmarks

arXiv AI

Jun 30

Low-cost concept-based localized explanations: How far can we get with training-free approaches?

arXiv:2606. 29069v1 Announce Type: new Abstract: Concept-based Explainable AI (C-XAI) seeks human-understandable explanations grounded in semantic concepts, yet validation is limited by the scarcity of fine-grained concept annotations.

By Darian Fern\'andez-Guti\'errez, Rafael Bello, Marilyn Bello, Natalia D\'iaz-Rodr\'iguez

llms rag multimodal

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